Images and videos are often searched on the Internet by using computer programs called “search engines”. The search engines require input of some keywords describing the image to find results relate to the description. For example, in response to receipt of a keyword, such as, “apple”, a search engine may return images of a fruit, apple, as well as anything else it may recognize as apple, such as a logo of a company that goes by the name “Apple”.
To search for images, some search engines use advanced techniques. One such technique is “reverse image search”. Instead of keywords, reverse image search accepts an image upload to use for searching for similar images. For example, instead of typing the word “apple” an image of the fruit “apple” is uploaded so as to find additional images of the fruit “apple”.
These techniques have been enhanced by recent developments in image recognition technology. The enhancements recognize shapes, colors, etc. of still images or moving images, ascertain which elements are of primary focus and to return relevant “visually similar” results from digital libraries of images and videos or databases. However, the existing techniques are limited to the keywords and image searching. Accordingly, it is desirable to provide novel techniques that further enhance and improve reverse image searching.